1 // Generated file (from: svdf2.mod.py). Do not edit
CreateModel(Model * model)2 void CreateModel(Model *model) {
3 OperandType type5(Type::INT32, {});
4 OperandType type0(Type::TENSOR_FLOAT32, {2, 3});
5 OperandType type6(Type::TENSOR_FLOAT32, {2, 4});
6 OperandType type4(Type::TENSOR_FLOAT32, {2, 80});
7 OperandType type3(Type::TENSOR_FLOAT32, {4});
8 OperandType type2(Type::TENSOR_FLOAT32, {8, 10});
9 OperandType type1(Type::TENSOR_FLOAT32, {8, 3});
10 // Phase 1, operands
11 auto input = model->addOperand(&type0);
12 auto weights_feature = model->addOperand(&type1);
13 auto weights_time = model->addOperand(&type2);
14 auto bias = model->addOperand(&type3);
15 auto state_in = model->addOperand(&type4);
16 auto rank_param = model->addOperand(&type5);
17 auto activation_param = model->addOperand(&type5);
18 auto state_out = model->addOperand(&type4);
19 auto output = model->addOperand(&type6);
20 // Phase 2, operations
21 static int32_t rank_param_init[] = {2};
22 model->setOperandValue(rank_param, rank_param_init, sizeof(int32_t) * 1);
23 static int32_t activation_param_init[] = {0};
24 model->setOperandValue(activation_param, activation_param_init, sizeof(int32_t) * 1);
25 model->addOperation(ANEURALNETWORKS_SVDF, {input, weights_feature, weights_time, bias, state_in, rank_param, activation_param}, {state_out, output});
26 // Phase 3, inputs and outputs
27 model->identifyInputsAndOutputs(
28 {input, weights_feature, weights_time, bias, state_in},
29 {state_out, output});
30 assert(model->isValid());
31 }
32
is_ignored(int i)33 bool is_ignored(int i) {
34 static std::set<int> ignore = {0};
35 return ignore.find(i) != ignore.end();
36 }
37